Vision transformers (ViTs) are recognized for their success in image classification [1]}, [2]} and WSOL [3]}. They consist of \(T\)  cascaded encoder blocks, each containing multi-headed attention, followed by a multi-layer perceptron (MLP). First, an input image \(x\)  of size \(W \times H\)  is divided into \(N\)  patches of resolution \((W/S)\times (H/S)\) , where \(S\)  denotes the patch size in pixels. These patches are then linearly projected to a fixed embedding size \(D\)  along with a learnable class token: \(f=\lbrace [\texttt {class} ]^{1\times D}, t_{1}^{1\times D}, t_{2}^{1\times D}, \dots , t_{N}^{1\times D}\rbrace \)  and passed through \(T\)  cascaded blocks. Furthermore, the features of a class token from \(T^{th}\)  block are fed to the MLP for class prediction, defined as \(p=Softmax_{\tau =1}(MLP(T_{vit}(x))\) , where
\(Softmax_{\tau }(s) = \frac{\exp (s / \tau )}{\sum _{k=1}^K \exp (s_k / \tau )}\) 
